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Identification and Management of Epidemic Hazard Areas for Urban Sustainability: A Case Study of Tongzhou, China

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  • Ming Sun

    (College of Landscape Architecture, Northeast Forestry University, Harbin 150040, China)

  • Tiange Xu

    (College of Landscape Architecture, Northeast Forestry University, Harbin 150040, China)

Abstract

The global epidemic is relatively stable, but urban pandemics will still exist. This study used sDNA (spatial design network analysis), spatial autocorrelation, and GWR (geographically weighted regression analysis) to identify potentially risky roads, pandemic hazard areas, and various infrastructure hazard areas in the Tongzhou District for urban sustainability. The results show that urban roads at risk during an epidemic have high proximity and aggregation effects. These roads are mainly concentrated in the core area. The hazard identification areas are focused on the urban sub-center and Yizhuang New Town. This paper derives the actual hazard areas using the POI (points of interest) data of COVID-19 (coronavirus disease 2019) and compares the results with the hazard identification areas. It is found that the hazard identification areas do not show the actual hazard area completely. In this study, GWR analyses based on gridded data of infrastructure POI proximity are used to obtain the hazard areas of various infrastructure types and develop different control ranges and methods. This provides new perspectives for identifying priority areas for epidemic prevention, control, and sustainable urban development.

Suggested Citation

  • Ming Sun & Tiange Xu, 2024. "Identification and Management of Epidemic Hazard Areas for Urban Sustainability: A Case Study of Tongzhou, China," Sustainability, MDPI, vol. 16(18), pages 1-25, September.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:18:p:7945-:d:1476095
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    References listed on IDEAS

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    1. Krzysztof Rząsa & Mateusz Ciski, 2022. "Influence of the Demographic, Social, and Environmental Factors on the COVID-19 Pandemic—Analysis of the Local Variations Using Geographically Weighted Regression," IJERPH, MDPI, vol. 19(19), pages 1-26, September.
    2. Xufang Zhang & Yu Yang & Minghua Zhao & Rongqing Han & Shijie Yang & Xiaojie Wang & Xiantao Tang & Weijuan Qu, 2022. "Trade-Off Analyses of Multiple Ecosystem Services and Their Drivers in the Shandong Yellow River Basin," IJERPH, MDPI, vol. 19(23), pages 1-29, November.
    3. Seungil Yum, 2023. "Analyses of human responses to Winter storm Kai using the GWR model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(2), pages 1805-1821, March.
    4. Haixia Feng & Zhouhao Wu & Xin Li & Huacai Xian & Qiang Jia & Xingyu Wang & Maoxin Zhu, 2023. "Effect of Transportation Operation on Air Quality in China Based on MODIS AOD during the Epidemic," Sustainability, MDPI, vol. 15(5), pages 1-15, February.
    5. Nushrat Nazia & Zahid Ahmad Butt & Melanie Lyn Bedard & Wang-Choi Tang & Hibah Sehar & Jane Law, 2022. "Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review," IJERPH, MDPI, vol. 19(14), pages 1-28, July.
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